Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations634351
Missing cells2179774
Missing cells (%)9.8%
Duplicate rows294795
Duplicate rows (%)46.5%
Total size in memory169.4 MiB
Average record size in memory280.0 B

Variable types

Numeric12
Text6
Categorical12
DateTime5

Alerts

nombretramo has constant value "Por Defnir" Constant
created_user has constant value "SIG_INVIAS" Constant
created_date has constant value "2025-05-02 05:01:01" Constant
last_edited_user has constant value "SIG_INVIAS" Constant
last_edited_date has constant value "2025-05-02 05:01:01" Constant
Dataset has 294795 (46.5%) duplicate rowsDuplicates
administrador is highly overall correlated with grupo_administrador_vialHigh correlation
calzada is highly overall correlated with categoria and 1 other fieldsHigh correlation
cantidadexentos787 is highly overall correlated with cantidadtraficoHigh correlation
cantidadtrafico is highly overall correlated with cantidadexentos787High correlation
categoria is highly overall correlated with calzadaHigh correlation
grupo_administrador_vial is highly overall correlated with administrador and 2 other fieldsHigh correlation
idpeaje is highly overall correlated with nombre_ruta and 1 other fieldsHigh correlation
nombre_ruta is highly overall correlated with idpeaje and 2 other fieldsHigh correlation
objectid is highly overall correlated with calzadaHigh correlation
poste_de_referencia_final is highly overall correlated with poste_de_referencia_inicialHigh correlation
poste_de_referencia_inicial is highly overall correlated with poste_de_referencia_finalHigh correlation
ruta is highly overall correlated with grupo_administrador_vial and 4 other fieldsHigh correlation
superficie is highly overall correlated with nombre_ruta and 1 other fieldsHigh correlation
territorial is highly overall correlated with grupo_administrador_vial and 1 other fieldsHigh correlation
categoria is highly imbalanced (66.5%) Imbalance
superficie is highly imbalanced (65.0%) Imbalance
fuente is highly imbalanced (56.4%) Imbalance
cantidadevasores has 98166 (15.5%) missing values Missing
cantidadexentos787 has 97703 (15.4%) missing values Missing
ubicacion has 24588 (3.9%) missing values Missing
objectid has 71877 (11.3%) missing values Missing
categoria has 71877 (11.3%) missing values Missing
poste_de_referencia_inicial has 71877 (11.3%) missing values Missing
distancia_inicial has 71877 (11.3%) missing values Missing
poste_de_referencia_final has 71877 (11.3%) missing values Missing
distancia_final has 71877 (11.3%) missing values Missing
nombre_ruta has 71877 (11.3%) missing values Missing
sector has 71877 (11.3%) missing values Missing
administrador has 71877 (11.3%) missing values Missing
grupo_administrador_vial has 449900 (70.9%) missing values Missing
superficie has 71877 (11.3%) missing values Missing
calzada has 71877 (11.3%) missing values Missing
ruta has 71877 (11.3%) missing values Missing
fuente has 71877 (11.3%) missing values Missing
globalid has 71877 (11.3%) missing values Missing
nombretramo has 71877 (11.3%) missing values Missing
created_user has 71877 (11.3%) missing values Missing
created_date has 71877 (11.3%) missing values Missing
last_edited_user has 71877 (11.3%) missing values Missing
last_edited_date has 71877 (11.3%) missing values Missing
territorial has 71877 (11.3%) missing values Missing
shape__length has 71877 (11.3%) missing values Missing
cantidadevasores is highly skewed (γ1 = 180.1816019) Skewed
valortarifa has 25976 (4.1%) zeros Zeros
cantidadtrafico has 13625 (2.1%) zeros Zeros
cantidadevasores has 529685 (83.5%) zeros Zeros
cantidadexentos787 has 255344 (40.3%) zeros Zeros
poste_de_referencia_inicial has 204345 (32.2%) zeros Zeros
distancia_inicial has 311133 (49.0%) zeros Zeros
poste_de_referencia_final has 24644 (3.9%) zeros Zeros
distancia_final has 140019 (22.1%) zeros Zeros
territorial has 29885 (4.7%) zeros Zeros

Reproduction

Analysis started2025-05-29 02:01:10.708222
Analysis finished2025-05-29 02:03:12.867753
Duration2 minutes and 2.16 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

idpeaje
Real number (ℝ)

High correlation 

Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.461973
Minimum1
Maximum221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:13.003349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q126
median49
Q379
95-th percentile135
Maximum221
Range220
Interquartile range (IQR)53

Descriptive statistics

Standard deviation38.69535
Coefficient of variation (CV)0.68533472
Kurtosis0.96888243
Mean56.461973
Median Absolute Deviation (MAD)24
Skewness1.0956108
Sum35816709
Variance1497.3301
MonotonicityNot monotonic
2025-05-29T02:03:13.185188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 32428
 
5.1%
23 24705
 
3.9%
39 24060
 
3.8%
25 22302
 
3.5%
50 21249
 
3.3%
16 20664
 
3.3%
54 15505
 
2.4%
27 14945
 
2.4%
26 12796
 
2.0%
15 12768
 
2.0%
Other values (141) 432929
68.2%
ValueCountFrequency (%)
1 1814
 
0.3%
2 2586
 
0.4%
3 6555
1.0%
4 5466
0.9%
5 2612
 
0.4%
6 2094
 
0.3%
7 920
 
0.1%
8 6048
1.0%
9 2968
0.5%
10 2829
0.4%
ValueCountFrequency (%)
221 80
 
< 0.1%
215 190
 
< 0.1%
185 884
0.1%
184 416
 
0.1%
183 240
 
< 0.1%
178 1212
0.2%
176 1242
0.2%
173 606
0.1%
172 123
 
< 0.1%
171 1437
0.2%

peaje
Text

Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:13.619074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length14
Mean length8.4234533
Min length4

Characters and Unicode

Total characters5343426
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALVARADO
2nd rowALVARADO
3rd rowALVARADO
4th rowALVARADO
5th rowALVARADO
ValueCountFrequency (%)
el 53735
 
6.3%
loboguerrero 32428
 
3.8%
los 28552
 
3.3%
la 28375
 
3.3%
roble 24705
 
2.9%
amagá 24060
 
2.8%
albarracín 22302
 
2.6%
caimanera 21249
 
2.5%
andes 20664
 
2.4%
puerto 17015
 
2.0%
Other values (162) 582278
68.1%
2025-05-29T02:03:14.424016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 910427
17.0%
R 525399
 
9.8%
O 455438
 
8.5%
E 424926
 
8.0%
L 334488
 
6.3%
C 288869
 
5.4%
S 279988
 
5.2%
I 274159
 
5.1%
N 262040
 
4.9%
221012
 
4.1%
Other values (25) 1366680
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5343426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 910427
17.0%
R 525399
 
9.8%
O 455438
 
8.5%
E 424926
 
8.0%
L 334488
 
6.3%
C 288869
 
5.4%
S 279988
 
5.2%
I 274159
 
5.1%
N 262040
 
4.9%
221012
 
4.1%
Other values (25) 1366680
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5343426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 910427
17.0%
R 525399
 
9.8%
O 455438
 
8.5%
E 424926
 
8.0%
L 334488
 
6.3%
C 288869
 
5.4%
S 279988
 
5.2%
I 274159
 
5.1%
N 262040
 
4.9%
221012
 
4.1%
Other values (25) 1366680
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5343426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 910427
17.0%
R 525399
 
9.8%
O 455438
 
8.5%
E 424926
 
8.0%
L 334488
 
6.3%
C 288869
 
5.4%
S 279988
 
5.2%
I 274159
 
5.1%
N 262040
 
4.9%
221012
 
4.1%
Other values (25) 1366680
25.6%

categoriatarifa
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
I
71023 
II
70932 
III
70877 
V
70297 
IV
70194 
Other values (45)
281028 

Length

Max length6
Median length2
Mean length2.1039235
Min length1

Characters and Unicode

Total characters1334626
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowII
3rd rowIII
4th rowIV
5th rowV

Common Values

ValueCountFrequency (%)
I 71023
11.2%
II 70932
11.2%
III 70877
11.2%
V 70297
11.1%
IV 70194
11.1%
IE 36412
 
5.7%
EG 36026
 
5.7%
ER 35833
 
5.6%
EA 33530
 
5.3%
VI 31740
 
5.0%
Other values (40) 107487
16.9%

Length

2025-05-29T02:03:15.008258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i 71023
11.2%
ii 70932
11.2%
iii 70877
11.2%
v 70297
11.1%
iv 70194
11.1%
ie 36412
 
5.7%
eg 36026
 
5.7%
er 35833
 
5.6%
ea 33530
 
5.3%
vi 31740
 
5.0%
Other values (40) 107487
16.9%

Most occurring characters

ValueCountFrequency (%)
I 737354
55.2%
V 214116
 
16.0%
E 213974
 
16.0%
A 44879
 
3.4%
G 36026
 
2.7%
R 35833
 
2.7%
C 15220
 
1.1%
P 9946
 
0.7%
O 9946
 
0.7%
L 9946
 
0.7%
Other values (8) 7386
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1334626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 737354
55.2%
V 214116
 
16.0%
E 213974
 
16.0%
A 44879
 
3.4%
G 36026
 
2.7%
R 35833
 
2.7%
C 15220
 
1.1%
P 9946
 
0.7%
O 9946
 
0.7%
L 9946
 
0.7%
Other values (8) 7386
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1334626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 737354
55.2%
V 214116
 
16.0%
E 213974
 
16.0%
A 44879
 
3.4%
G 36026
 
2.7%
R 35833
 
2.7%
C 15220
 
1.1%
P 9946
 
0.7%
O 9946
 
0.7%
L 9946
 
0.7%
Other values (8) 7386
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1334626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 737354
55.2%
V 214116
 
16.0%
E 213974
 
16.0%
A 44879
 
3.4%
G 36026
 
2.7%
R 35833
 
2.7%
C 15220
 
1.1%
P 9946
 
0.7%
O 9946
 
0.7%
L 9946
 
0.7%
Other values (8) 7386
 
0.6%

desde
Date

Distinct306
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Minimum2014-01-01 00:00:00
Maximum2024-05-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-29T02:03:15.353583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:03:15.482093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

hasta
Date

Distinct273
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Minimum2014-01-04 00:00:00
Maximum2024-05-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-29T02:03:15.615487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:03:15.744836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

valortarifa
Real number (ℝ)

Zeros 

Distinct805
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16938.656
Minimum0
Maximum161600
Zeros25976
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:15.872209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile500
Q17100
median11100
Q324200
95-th percentile47100
Maximum161600
Range161600
Interquartile range (IQR)17100

Descriptive statistics

Standard deviation15113.364
Coefficient of variation (CV)0.89224102
Kurtosis4.9960572
Mean16938.656
Median Absolute Deviation (MAD)6700
Skewness1.796389
Sum1.0745053 × 1010
Variance2.2841377 × 108
MonotonicityNot monotonic
2025-05-29T02:03:16.002927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25976
 
4.1%
7100 8214
 
1.3%
8400 8133
 
1.3%
9600 6795
 
1.1%
8700 6442
 
1.0%
9300 5912
 
0.9%
8100 5906
 
0.9%
9100 5625
 
0.9%
7700 5448
 
0.9%
7200 5280
 
0.8%
Other values (795) 550620
86.8%
ValueCountFrequency (%)
0 25976
4.1%
200 3824
 
0.6%
300 400
 
0.1%
400 1424
 
0.2%
500 1124
 
0.2%
600 3740
 
0.6%
700 5274
 
0.8%
800 1836
 
0.3%
900 323
 
0.1%
1000 1574
 
0.2%
ValueCountFrequency (%)
161600 5
 
< 0.1%
161300 2
 
< 0.1%
150700 5
 
< 0.1%
150400 2
 
< 0.1%
145500 5
 
< 0.1%
145200 2
 
< 0.1%
142700 41
< 0.1%
135800 5
 
< 0.1%
135500 2
 
< 0.1%
135100 26
< 0.1%

cantidadtrafico
Real number (ℝ)

High correlation  Zeros 

Distinct39582
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23641.344
Minimum0
Maximum1124159
Zeros13625
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:16.146793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q1304
median4046
Q315698
95-th percentile101621
Maximum1124159
Range1124159
Interquartile range (IQR)15394

Descriptive statistics

Standard deviation72350.704
Coefficient of variation (CV)3.0603465
Kurtosis73.257493
Mean23641.344
Median Absolute Deviation (MAD)4007
Skewness7.4825358
Sum1.499691 × 1010
Variance5.2346244 × 109
MonotonicityNot monotonic
2025-05-29T02:03:16.280375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13625
 
2.1%
1 4095
 
0.6%
2 3314
 
0.5%
4 2448
 
0.4%
3 2377
 
0.4%
6 2345
 
0.4%
10 2095
 
0.3%
5 2041
 
0.3%
14 1912
 
0.3%
13 1879
 
0.3%
Other values (39572) 598220
94.3%
ValueCountFrequency (%)
0 13625
2.1%
1 4095
 
0.6%
2 3314
 
0.5%
3 2377
 
0.4%
4 2448
 
0.4%
5 2041
 
0.3%
6 2345
 
0.4%
7 1876
 
0.3%
8 1856
 
0.3%
9 1358
 
0.2%
ValueCountFrequency (%)
1124159 18
< 0.1%
1120843 18
< 0.1%
1055640 18
< 0.1%
1050858 18
< 0.1%
1046549 9
< 0.1%
1042673 18
< 0.1%
1028714 18
< 0.1%
1002112 18
< 0.1%
1001840 18
< 0.1%
993263 18
< 0.1%

cantidadevasores
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct83
Distinct (%)< 0.1%
Missing98166
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean0.1897927
Minimum0
Maximum4006
Zeros529685
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:16.427676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4006
Range4006
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.2531
Coefficient of variation (CV)80.367159
Kurtosis38797.356
Mean0.1897927
Median Absolute Deviation (MAD)0
Skewness180.1816
Sum101764
Variance232.65707
MonotonicityNot monotonic
2025-05-29T02:03:16.573077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 529685
83.5%
1 3035
 
0.5%
2 897
 
0.1%
3 761
 
0.1%
4 392
 
0.1%
5 296
 
< 0.1%
6 222
 
< 0.1%
7 146
 
< 0.1%
8 120
 
< 0.1%
9 68
 
< 0.1%
Other values (73) 563
 
0.1%
(Missing) 98166
 
15.5%
ValueCountFrequency (%)
0 529685
83.5%
1 3035
 
0.5%
2 897
 
0.1%
3 761
 
0.1%
4 392
 
0.1%
5 296
 
< 0.1%
6 222
 
< 0.1%
7 146
 
< 0.1%
8 120
 
< 0.1%
9 68
 
< 0.1%
ValueCountFrequency (%)
4006 2
 
< 0.1%
3726 2
 
< 0.1%
2470 3
< 0.1%
2151 3
< 0.1%
1641 6
< 0.1%
996 2
 
< 0.1%
764 2
 
< 0.1%
733 6
< 0.1%
666 4
< 0.1%
614 3
< 0.1%

cantidadexentos787
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct3841
Distinct (%)0.7%
Missing97703
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean223.26961
Minimum0
Maximum101300
Zeros255344
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:16.719044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q361
95-th percentile1470
Maximum101300
Range101300
Interquartile range (IQR)61

Descriptive statistics

Standard deviation726.50309
Coefficient of variation (CV)3.2539274
Kurtosis1561.8028
Mean223.26961
Median Absolute Deviation (MAD)1
Skewness16.202759
Sum1.1981719 × 108
Variance527806.75
MonotonicityNot monotonic
2025-05-29T02:03:16.871353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 255344
40.3%
2 22484
 
3.5%
1 20129
 
3.2%
4 11793
 
1.9%
3 10044
 
1.6%
5 6895
 
1.1%
6 6713
 
1.1%
8 5105
 
0.8%
7 4519
 
0.7%
10 3510
 
0.6%
Other values (3831) 190112
30.0%
(Missing) 97703
 
15.4%
ValueCountFrequency (%)
0 255344
40.3%
1 20129
 
3.2%
2 22484
 
3.5%
3 10044
 
1.6%
4 11793
 
1.9%
5 6895
 
1.1%
6 6713
 
1.1%
7 4519
 
0.7%
8 5105
 
0.8%
9 3401
 
0.5%
ValueCountFrequency (%)
101300 2
 
< 0.1%
55327 2
 
< 0.1%
27410 2
 
< 0.1%
23670 8
< 0.1%
14600 2
 
< 0.1%
10230 2
 
< 0.1%
9326 1
 
< 0.1%
7641 4
< 0.1%
6382 6
< 0.1%
6208 3
 
< 0.1%
Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:17.247414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length14
Mean length8.4234533
Min length4

Characters and Unicode

Total characters5343426
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalvarado
2nd rowalvarado
3rd rowalvarado
4th rowalvarado
5th rowalvarado
ValueCountFrequency (%)
el 53735
 
6.3%
loboguerrero 32428
 
3.8%
los 28552
 
3.3%
la 28375
 
3.3%
roble 24705
 
2.9%
amaga 24060
 
2.8%
albarracin 22302
 
2.6%
caimanera 21249
 
2.5%
andes 20664
 
2.4%
puerto 17015
 
2.0%
Other values (161) 582278
68.1%
2025-05-29T02:03:18.910098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 973473
18.2%
r 525399
9.8%
o 467790
 
8.8%
e 424926
 
8.0%
l 334488
 
6.3%
i 318801
 
6.0%
c 288869
 
5.4%
s 279988
 
5.2%
n 273378
 
5.1%
221012
 
4.1%
Other values (21) 1235302
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5343426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 973473
18.2%
r 525399
9.8%
o 467790
 
8.8%
e 424926
 
8.0%
l 334488
 
6.3%
i 318801
 
6.0%
c 288869
 
5.4%
s 279988
 
5.2%
n 273378
 
5.1%
221012
 
4.1%
Other values (21) 1235302
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5343426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 973473
18.2%
r 525399
9.8%
o 467790
 
8.8%
e 424926
 
8.0%
l 334488
 
6.3%
i 318801
 
6.0%
c 288869
 
5.4%
s 279988
 
5.2%
n 273378
 
5.1%
221012
 
4.1%
Other values (21) 1235302
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5343426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 973473
18.2%
r 525399
9.8%
o 467790
 
8.8%
e 424926
 
8.0%
l 334488
 
6.3%
i 318801
 
6.0%
c 288869
 
5.4%
s 279988
 
5.2%
n 273378
 
5.1%
221012
 
4.1%
Other values (21) 1235302
23.1%
Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:19.404642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.0218286
Min length0

Characters and Unicode

Total characters2551251
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4305
2nd row4305
3rd row4305
4th row4305
5th row4305
ValueCountFrequency (%)
5501 67671
 
10.6%
4001 32428
 
5.1%
6206 27741
 
4.4%
2513 25860
 
4.1%
nan 24897
 
3.9%
6003 24060
 
3.8%
2505 22520
 
3.5%
9004 21249
 
3.3%
2103 19384
 
3.1%
2515 17697
 
2.8%
Other values (82) 351938
55.4%
2025-05-29T02:03:19.812709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 746983
29.3%
5 502422
19.7%
1 253189
 
9.9%
2 212448
 
8.3%
4 206443
 
8.1%
6 184479
 
7.2%
9 122988
 
4.8%
3 91759
 
3.6%
n 53999
 
2.1%
7 49583
 
1.9%
Other values (9) 126958
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2551251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 746983
29.3%
5 502422
19.7%
1 253189
 
9.9%
2 212448
 
8.3%
4 206443
 
8.1%
6 184479
 
7.2%
9 122988
 
4.8%
3 91759
 
3.6%
n 53999
 
2.1%
7 49583
 
1.9%
Other values (9) 126958
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2551251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 746983
29.3%
5 502422
19.7%
1 253189
 
9.9%
2 212448
 
8.3%
4 206443
 
8.1%
6 184479
 
7.2%
9 122988
 
4.8%
3 91759
 
3.6%
n 53999
 
2.1%
7 49583
 
1.9%
Other values (9) 126958
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2551251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 746983
29.3%
5 502422
19.7%
1 253189
 
9.9%
2 212448
 
8.3%
4 206443
 
8.1%
6 184479
 
7.2%
9 122988
 
4.8%
3 91759
 
3.6%
n 53999
 
2.1%
7 49583
 
1.9%
Other values (9) 126958
 
5.0%

ubicacion
Text

Missing 

Distinct115
Distinct (%)< 0.1%
Missing24588
Missing (%)3.9%
Memory size4.8 MiB
2025-05-29T02:03:20.184056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length121
Median length61
Mean length43.996089
Min length0

Characters and Unicode

Total characters26827187
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVía Andalucía – Y de Cerritos PR2506 Km 86
2nd rowVía Andalucía – Y de Cerritos PR2506 Km 86
3rd rowVía Andalucía – Y de Cerritos PR2506 Km 86
4th rowVía Andalucía – Y de Cerritos PR2506 Km 86
5th rowVía Andalucía – Y de Cerritos PR2506 Km 86
ValueCountFrequency (%)
km 475666
 
9.5%
vía 377940
 
7.5%
343190
 
6.8%
287032
 
5.7%
de 99716
 
2.0%
en 97284
 
1.9%
el 94773
 
1.9%
ruta 86855
 
1.7%
la 86353
 
1.7%
bogotá 72615
 
1.4%
Other values (328) 2991382
59.7%
2025-05-29T02:03:21.684031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4486651
 
16.7%
a 2353882
 
8.8%
e 1336897
 
5.0%
i 1291096
 
4.8%
o 1151684
 
4.3%
r 1142732
 
4.3%
n 1080572
 
4.0%
0 966657
 
3.6%
l 754123
 
2.8%
u 694238
 
2.6%
Other values (62) 11568655
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26827187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4486651
 
16.7%
a 2353882
 
8.8%
e 1336897
 
5.0%
i 1291096
 
4.8%
o 1151684
 
4.3%
r 1142732
 
4.3%
n 1080572
 
4.0%
0 966657
 
3.6%
l 754123
 
2.8%
u 694238
 
2.6%
Other values (62) 11568655
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26827187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4486651
 
16.7%
a 2353882
 
8.8%
e 1336897
 
5.0%
i 1291096
 
4.8%
o 1151684
 
4.3%
r 1142732
 
4.3%
n 1080572
 
4.0%
0 966657
 
3.6%
l 754123
 
2.8%
u 694238
 
2.6%
Other values (62) 11568655
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26827187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4486651
 
16.7%
a 2353882
 
8.8%
e 1336897
 
5.0%
i 1291096
 
4.8%
o 1151684
 
4.3%
r 1142732
 
4.3%
n 1080572
 
4.0%
0 966657
 
3.6%
l 754123
 
2.8%
u 694238
 
2.6%
Other values (62) 11568655
43.1%

objectid
Real number (ℝ)

High correlation  Missing 

Distinct204
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean1131.7784
Minimum7
Maximum11433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:21.821592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile47
Q1166
median401
Q3524
95-th percentile9833
Maximum11433
Range11426
Interquartile range (IQR)358

Descriptive statistics

Standard deviation2595.5681
Coefficient of variation (CV)2.2933537
Kurtosis7.9000473
Mean1131.7784
Median Absolute Deviation (MAD)173
Skewness3.0826295
Sum6.3659592 × 108
Variance6736974
MonotonicityNot monotonic
2025-05-29T02:03:21.959562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
294 8118
 
1.3%
449 7519
 
1.2%
450 7519
 
1.2%
10232 7519
 
1.2%
467 7519
 
1.2%
9831 7519
 
1.2%
9833 7519
 
1.2%
10231 7519
 
1.2%
448 7519
 
1.2%
466 7519
 
1.2%
Other values (194) 486685
76.7%
(Missing) 71877
 
11.3%
ValueCountFrequency (%)
7 882
 
0.1%
8 826
 
0.1%
17 1474
 
0.2%
27 826
 
0.1%
28 3192
0.5%
34 2070
0.3%
36 2215
0.3%
37 4504
0.7%
38 2215
0.3%
39 2019
0.3%
ValueCountFrequency (%)
11433 2327
 
0.4%
11432 2327
 
0.4%
11032 2327
 
0.4%
10232 7519
1.2%
10231 7519
1.2%
9833 7519
1.2%
9831 7519
1.2%
9031 2361
 
0.4%
8233 1190
 
0.2%
5431 3963
0.6%

categoria
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
1
486065 
Por Definir
65821 
3
 
6334
4
 
4254

Length

Max length11
Median length1
Mean length2.1702052
Min length1

Characters and Unicode

Total characters1220684
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 486065
76.6%
Por Definir 65821
 
10.4%
3 6334
 
1.0%
4 4254
 
0.7%
(Missing) 71877
 
11.3%

Length

2025-05-29T02:03:22.084907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:22.171458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 486065
77.4%
por 65821
 
10.5%
definir 65821
 
10.5%
3 6334
 
1.0%
4 4254
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 486065
39.8%
r 131642
 
10.8%
i 131642
 
10.8%
P 65821
 
5.4%
o 65821
 
5.4%
65821
 
5.4%
e 65821
 
5.4%
D 65821
 
5.4%
f 65821
 
5.4%
n 65821
 
5.4%
Other values (2) 10588
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1220684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 486065
39.8%
r 131642
 
10.8%
i 131642
 
10.8%
P 65821
 
5.4%
o 65821
 
5.4%
65821
 
5.4%
e 65821
 
5.4%
D 65821
 
5.4%
f 65821
 
5.4%
n 65821
 
5.4%
Other values (2) 10588
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1220684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 486065
39.8%
r 131642
 
10.8%
i 131642
 
10.8%
P 65821
 
5.4%
o 65821
 
5.4%
65821
 
5.4%
e 65821
 
5.4%
D 65821
 
5.4%
f 65821
 
5.4%
n 65821
 
5.4%
Other values (2) 10588
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1220684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 486065
39.8%
r 131642
 
10.8%
i 131642
 
10.8%
P 65821
 
5.4%
o 65821
 
5.4%
65821
 
5.4%
e 65821
 
5.4%
D 65821
 
5.4%
f 65821
 
5.4%
n 65821
 
5.4%
Other values (2) 10588
 
0.9%

poste_de_referencia_inicial
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct69
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean32.408794
Minimum0
Maximum131
Zeros204345
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:22.274131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19
Q359
95-th percentile108
Maximum131
Range131
Interquartile range (IQR)59

Descriptive statistics

Standard deviation35.363942
Coefficient of variation (CV)1.0911835
Kurtosis-0.56474388
Mean32.408794
Median Absolute Deviation (MAD)19
Skewness0.78750337
Sum18229104
Variance1250.6084
MonotonicityNot monotonic
2025-05-29T02:03:22.402991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 204345
32.2%
59 43010
 
6.8%
25 19048
 
3.0%
19 18896
 
3.0%
108 17416
 
2.7%
62 16562
 
2.6%
2 13752
 
2.2%
39 9805
 
1.5%
10 9165
 
1.4%
18 8418
 
1.3%
Other values (59) 202057
31.9%
(Missing) 71877
 
11.3%
ValueCountFrequency (%)
0 204345
32.2%
2 13752
 
2.2%
3 2836
 
0.4%
5 7470
 
1.2%
6 5527
 
0.9%
7 2146
 
0.3%
9 666
 
0.1%
10 9165
 
1.4%
12 3530
 
0.6%
14 4430
 
0.7%
ValueCountFrequency (%)
131 2076
 
0.3%
117 4390
 
0.7%
114 3963
 
0.6%
109 2863
 
0.5%
108 17416
2.7%
98 3963
 
0.6%
97 3963
 
0.6%
95 3963
 
0.6%
93 4767
 
0.8%
92 2406
 
0.4%

distancia_inicial
Real number (ℝ)

Missing  Zeros 

Distinct68
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean254.80158
Minimum0
Maximum2775
Zeros311133
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:22.524215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3441
95-th percentile964
Maximum2775
Range2775
Interquartile range (IQR)441

Descriptive statistics

Standard deviation409.39206
Coefficient of variation (CV)1.6067092
Kurtosis9.104311
Mean254.80158
Median Absolute Deviation (MAD)0
Skewness2.3785316
Sum1.4331927 × 108
Variance167601.86
MonotonicityNot monotonic
2025-05-29T02:03:22.667563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 311133
49.0%
800 15115
 
2.4%
500 14708
 
2.3%
180 9230
 
1.5%
964 7519
 
1.2%
1118 7519
 
1.2%
96 7519
 
1.2%
300 7463
 
1.2%
100 6910
 
1.1%
876 6465
 
1.0%
Other values (58) 168893
26.6%
(Missing) 71877
 
11.3%
ValueCountFrequency (%)
0 311133
49.0%
9 1190
 
0.2%
10 666
 
0.1%
13 1652
 
0.3%
24 2406
 
0.4%
40 1520
 
0.2%
49 3963
 
0.6%
60 2215
 
0.3%
86 826
 
0.1%
96 7519
 
1.2%
ValueCountFrequency (%)
2775 4188
0.7%
1136 3963
0.6%
1130 2215
 
0.3%
1118 7519
1.2%
1000 3998
0.6%
980 4201
0.7%
964 7519
1.2%
942 4846
0.8%
937 2361
 
0.4%
925 4188
0.7%

poste_de_referencia_final
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct103
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean64.645952
Minimum0
Maximum149
Zeros24644
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:22.792780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q134
median62
Q399
95-th percentile120
Maximum149
Range149
Interquartile range (IQR)65

Descriptive statistics

Standard deviation38.729998
Coefficient of variation (CV)0.59910941
Kurtosis-1.0751283
Mean64.645952
Median Absolute Deviation (MAD)35
Skewness-0.019872148
Sum36361667
Variance1500.0128
MonotonicityNot monotonic
2025-05-29T02:03:22.917531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 51490
 
8.1%
0 24644
 
3.9%
62 23932
 
3.8%
25 19048
 
3.0%
72 14169
 
2.2%
19 13569
 
2.1%
59 12934
 
2.0%
86 12622
 
2.0%
87 12134
 
1.9%
35 11763
 
1.9%
Other values (93) 366169
57.7%
(Missing) 71877
 
11.3%
ValueCountFrequency (%)
0 24644
3.9%
2 10560
1.7%
3 1306
 
0.2%
5 10594
1.7%
6 5527
 
0.9%
7 2146
 
0.3%
8 1432
 
0.2%
9 666
 
0.1%
10 9165
 
1.4%
12 3530
 
0.6%
ValueCountFrequency (%)
149 2000
 
0.3%
145 3192
0.5%
140 2972
0.5%
138 5899
0.9%
135 1306
 
0.2%
134 826
 
0.1%
131 2076
 
0.3%
124 68
 
< 0.1%
123 1530
 
0.2%
122 4201
0.7%

distancia_final
Real number (ℝ)

Missing  Zeros 

Distinct107
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean424.06376
Minimum0
Maximum3045
Zeros140019
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:23.060368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median300
Q3697
95-th percentile1118
Maximum3045
Range3045
Interquartile range (IQR)666

Descriptive statistics

Standard deviation478.49226
Coefficient of variation (CV)1.1283498
Kurtosis8.8204579
Mean424.06376
Median Absolute Deviation (MAD)300
Skewness2.26795
Sum2.3852484 × 108
Variance228954.84
MonotonicityNot monotonic
2025-05-29T02:03:23.207776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 140019
22.1%
96 37595
 
5.9%
500 26894
 
4.2%
408 13266
 
2.1%
180 9230
 
1.5%
600 8416
 
1.3%
55 8118
 
1.3%
250 7749
 
1.2%
695 7721
 
1.2%
1118 7519
 
1.2%
Other values (97) 295947
46.7%
(Missing) 71877
 
11.3%
ValueCountFrequency (%)
0 140019
22.1%
31 2406
 
0.4%
40 1520
 
0.2%
49 3963
 
0.6%
55 8118
 
1.3%
60 2215
 
0.3%
81 2457
 
0.4%
84 1910
 
0.3%
96 37595
 
5.9%
99.7 3192
 
0.5%
ValueCountFrequency (%)
3045 4188
0.7%
2775 4188
0.7%
1450 3998
0.6%
1360 4846
0.8%
1295 1227
 
0.2%
1150 1822
 
0.3%
1136 3963
0.6%
1130 2215
 
0.3%
1118 7519
1.2%
1000 3998
0.6%

nombre_ruta
Categorical

High correlation  Missing 

Distinct21
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
Troncal de Occidente
104739 
Troncal Central del Norte
77204 
Troncal del Magdalena
66237 
Transversal Buenaventura - Villavicencio - Puerto Carreño
64777 
Transversal Tribuga - Arauca
58241 
Other values (16)
191276 

Length

Max length78
Median length57
Mean length30.436166
Min length18

Characters and Unicode

Total characters17119552
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlternas a la Troncal del Magdalena
2nd rowAlternas a la Troncal del Magdalena
3rd rowAlternas a la Troncal del Magdalena
4th rowAlternas a la Troncal del Magdalena
5th rowAlternas a la Troncal del Magdalena

Common Values

ValueCountFrequency (%)
Troncal de Occidente 104739
16.5%
Troncal Central del Norte 77204
12.2%
Troncal del Magdalena 66237
10.4%
Transversal Buenaventura - Villavicencio - Puerto Carreño 64777
10.2%
Transversal Tribuga - Arauca 58241
9.2%
Transversal del Caribe 48205
7.6%
Alternas a la Transversal del Caribe 32554
 
5.1%
Troncal Villagarzon - Saravena 22600
 
3.6%
Transversal Medellin - Bogotá 14343
 
2.3%
Alternas a la Troncal de Occidente 12690
 
2.0%
Other values (11) 60884
9.6%
(Missing) 71877
11.3%

Length

2025-05-29T02:03:23.815234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
troncal 318190
12.8%
287887
 
11.6%
del 254628
 
10.3%
transversal 245112
 
9.9%
de 125016
 
5.0%
occidente 117429
 
4.7%
central 90898
 
3.7%
norte 90898
 
3.7%
puerto 83111
 
3.3%
caribe 80759
 
3.3%
Other values (34) 788859
31.8%

Most occurring characters

ValueCountFrequency (%)
a 2024189
11.8%
1920313
11.2%
e 1778321
10.4%
r 1646291
 
9.6%
l 1338632
 
7.8%
n 1240081
 
7.2%
o 787351
 
4.6%
c 775742
 
4.5%
T 623813
 
3.6%
d 593716
 
3.5%
Other values (31) 4391103
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17119552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2024189
11.8%
1920313
11.2%
e 1778321
10.4%
r 1646291
 
9.6%
l 1338632
 
7.8%
n 1240081
 
7.2%
o 787351
 
4.6%
c 775742
 
4.5%
T 623813
 
3.6%
d 593716
 
3.5%
Other values (31) 4391103
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17119552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2024189
11.8%
1920313
11.2%
e 1778321
10.4%
r 1646291
 
9.6%
l 1338632
 
7.8%
n 1240081
 
7.2%
o 787351
 
4.6%
c 775742
 
4.5%
T 623813
 
3.6%
d 593716
 
3.5%
Other values (31) 4391103
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17119552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2024189
11.8%
1920313
11.2%
e 1778321
10.4%
r 1646291
 
9.6%
l 1338632
 
7.8%
n 1240081
 
7.2%
o 787351
 
4.6%
c 775742
 
4.5%
T 623813
 
3.6%
d 593716
 
3.5%
Other values (31) 4391103
25.6%

sector
Text

Missing 

Distinct154
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
2025-05-29T02:03:24.157167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length76
Median length48
Mean length26
Min length9

Characters and Unicode

Total characters14624324
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIbagué - Mariquita
2nd rowIbagué - Mariquita
3rd rowIbagué - Mariquita
4th rowIbagué - Mariquita
5th rowIbagué - Mariquita
ValueCountFrequency (%)
435956
 
17.1%
por 93810
 
3.7%
paso 91449
 
3.6%
la 85538
 
3.4%
nacional 69797
 
2.7%
el 65894
 
2.6%
de 58937
 
2.3%
puente 55872
 
2.2%
chocontá 54779
 
2.2%
tunja 48419
 
1.9%
Other values (212) 1486204
58.4%
2025-05-29T02:03:25.371204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2019884
13.8%
a 1755279
 
12.0%
o 1115787
 
7.6%
e 975386
 
6.7%
r 834397
 
5.7%
n 697118
 
4.8%
i 677701
 
4.6%
l 570740
 
3.9%
t 528490
 
3.6%
u 488990
 
3.3%
Other values (52) 4960552
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14624324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2019884
13.8%
a 1755279
 
12.0%
o 1115787
 
7.6%
e 975386
 
6.7%
r 834397
 
5.7%
n 697118
 
4.8%
i 677701
 
4.6%
l 570740
 
3.9%
t 528490
 
3.6%
u 488990
 
3.3%
Other values (52) 4960552
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14624324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2019884
13.8%
a 1755279
 
12.0%
o 1115787
 
7.6%
e 975386
 
6.7%
r 834397
 
5.7%
n 697118
 
4.8%
i 677701
 
4.6%
l 570740
 
3.9%
t 528490
 
3.6%
u 488990
 
3.3%
Other values (52) 4960552
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14624324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2019884
13.8%
a 1755279
 
12.0%
o 1115787
 
7.6%
e 975386
 
6.7%
r 834397
 
5.7%
n 697118
 
4.8%
i 677701
 
4.6%
l 570740
 
3.9%
t 528490
 
3.6%
u 488990
 
3.3%
Other values (52) 4960552
33.9%

administrador
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
2.0
338004 
1.0
190602 
3.0
 
17090
5.0
 
10318
6.0
 
6460

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1687422
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 338004
53.3%
1.0 190602
30.0%
3.0 17090
 
2.7%
5.0 10318
 
1.6%
6.0 6460
 
1.0%
(Missing) 71877
 
11.3%

Length

2025-05-29T02:03:25.932414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:26.252224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 338004
60.1%
1.0 190602
33.9%
3.0 17090
 
3.0%
5.0 10318
 
1.8%
6.0 6460
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 562474
33.3%
0 562474
33.3%
2 338004
20.0%
1 190602
 
11.3%
3 17090
 
1.0%
5 10318
 
0.6%
6 6460
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1687422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 562474
33.3%
0 562474
33.3%
2 338004
20.0%
1 190602
 
11.3%
3 17090
 
1.0%
5 10318
 
0.6%
6 6460
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1687422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 562474
33.3%
0 562474
33.3%
2 338004
20.0%
1 190602
 
11.3%
3 17090
 
1.0%
5 10318
 
0.6%
6 6460
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1687422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 562474
33.3%
0 562474
33.3%
2 338004
20.0%
1 190602
 
11.3%
3 17090
 
1.0%
5 10318
 
0.6%
6 6460
 
0.4%

grupo_administrador_vial
Categorical

High correlation  Missing 

Distinct7
Distinct (%)< 0.1%
Missing449900
Missing (%)70.9%
Memory size4.8 MiB
1
98715 
2
35800 
20907 
3
18925 
8
 
6460
Other values (2)
 
3644

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters184451
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 98715
 
15.6%
2 35800
 
5.6%
20907
 
3.3%
3 18925
 
3.0%
8 6460
 
1.0%
5 3508
 
0.6%
4 136
 
< 0.1%
(Missing) 449900
70.9%

Length

2025-05-29T02:03:26.486946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:26.613554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 98715
60.4%
2 35800
 
21.9%
3 18925
 
11.6%
8 6460
 
4.0%
5 3508
 
2.1%
4 136
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 98715
53.5%
2 35800
 
19.4%
20907
 
11.3%
3 18925
 
10.3%
8 6460
 
3.5%
5 3508
 
1.9%
4 136
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 184451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 98715
53.5%
2 35800
 
19.4%
20907
 
11.3%
3 18925
 
10.3%
8 6460
 
3.5%
5 3508
 
1.9%
4 136
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 184451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 98715
53.5%
2 35800
 
19.4%
20907
 
11.3%
3 18925
 
10.3%
8 6460
 
3.5%
5 3508
 
1.9%
4 136
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 184451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 98715
53.5%
2 35800
 
19.4%
20907
 
11.3%
3 18925
 
10.3%
8 6460
 
3.5%
5 3508
 
1.9%
4 136
 
0.1%

superficie
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
3
502780 
50453 
1
 
9241

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters562474
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 502780
79.3%
50453
 
8.0%
1 9241
 
1.5%
(Missing) 71877
 
11.3%

Length

2025-05-29T02:03:26.719702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:26.797903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 502780
98.2%
1 9241
 
1.8%

Most occurring characters

ValueCountFrequency (%)
3 502780
89.4%
50453
 
9.0%
1 9241
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 562474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 502780
89.4%
50453
 
9.0%
1 9241
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 562474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 502780
89.4%
50453
 
9.0%
1 9241
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 562474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 502780
89.4%
50453
 
9.0%
1 9241
 
1.6%

calzada
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
1.0
245337 
2.0
235484 
3.0
81653 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1687422
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 245337
38.7%
2.0 235484
37.1%
3.0 81653
 
12.9%
(Missing) 71877
 
11.3%

Length

2025-05-29T02:03:26.876095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:26.943215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 245337
43.6%
2.0 235484
41.9%
3.0 81653
 
14.5%

Most occurring characters

ValueCountFrequency (%)
. 562474
33.3%
0 562474
33.3%
1 245337
14.5%
2 235484
14.0%
3 81653
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1687422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 562474
33.3%
0 562474
33.3%
1 245337
14.5%
2 235484
14.0%
3 81653
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1687422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 562474
33.3%
0 562474
33.3%
1 245337
14.5%
2 235484
14.0%
3 81653
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1687422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 562474
33.3%
0 562474
33.3%
1 245337
14.5%
2 235484
14.0%
3 81653
 
4.8%

ruta
Categorical

High correlation  Missing 

Distinct20
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
25
98005 
55
77204 
40
67698 
90
61375 
45
61299 
Other values (15)
196893 

Length

Max length2
Median length2
Mean length1.9173704
Min length1

Characters and Unicode

Total characters1078471
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43
2nd row43
3rd row43
4th row43
5th row43

Common Values

ValueCountFrequency (%)
25 98005
15.4%
55 77204
12.2%
40 67698
10.7%
90 61375
9.7%
45 61299
9.7%
46477
7.3%
50 27636
 
4.4%
65 22600
 
3.6%
62 19815
 
3.1%
21 19384
 
3.1%
Other values (10) 60981
9.6%
(Missing) 71877
11.3%

Length

2025-05-29T02:03:27.027490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25 98005
19.0%
55 77204
15.0%
40 67698
13.1%
90 61375
11.9%
45 61299
11.9%
50 27636
 
5.4%
65 22600
 
4.4%
62 19815
 
3.8%
21 19384
 
3.8%
29 12661
 
2.5%
Other values (9) 48320
9.4%

Most occurring characters

ValueCountFrequency (%)
5 375827
34.8%
0 173308
16.1%
2 156925
14.6%
4 135339
 
12.5%
9 76294
 
7.1%
6 74347
 
6.9%
46477
 
4.3%
1 19384
 
1.8%
3 8874
 
0.8%
8 6662
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1078471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 375827
34.8%
0 173308
16.1%
2 156925
14.6%
4 135339
 
12.5%
9 76294
 
7.1%
6 74347
 
6.9%
46477
 
4.3%
1 19384
 
1.8%
3 8874
 
0.8%
8 6662
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1078471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 375827
34.8%
0 173308
16.1%
2 156925
14.6%
4 135339
 
12.5%
9 76294
 
7.1%
6 74347
 
6.9%
46477
 
4.3%
1 19384
 
1.8%
3 8874
 
0.8%
8 6662
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1078471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 375827
34.8%
0 173308
16.1%
2 156925
14.6%
4 135339
 
12.5%
9 76294
 
7.1%
6 74347
 
6.9%
46477
 
4.3%
1 19384
 
1.8%
3 8874
 
0.8%
8 6662
 
0.6%

fuente
Categorical

Imbalance  Missing 

Distinct7
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
IGAC (Contrato 2932/2008)
434289 
WFS ANI
48984 
IGAC (Contrato 2130/2014)
47878 
Google Maps
 
15440
Google Maps, WFS ANI.
 
8402
Other values (2)
 
7481

Length

Max length37
Median length25
Mean length23.108677
Min length7

Characters and Unicode

Total characters12998030
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIGAC (Contrato 2130/2014)
2nd rowIGAC (Contrato 2130/2014)
3rd rowIGAC (Contrato 2130/2014)
4th rowIGAC (Contrato 2130/2014)
5th rowIGAC (Contrato 2130/2014)

Common Values

ValueCountFrequency (%)
IGAC (Contrato 2932/2008) 434289
68.5%
WFS ANI 48984
 
7.7%
IGAC (Contrato 2130/2014) 47878
 
7.5%
Google Maps 15440
 
2.4%
Google Maps, WFS ANI. 8402
 
1.3%
Google Maps (DC Criterio Técnico) 5528
 
0.9%
Archivos SHP suministrados por la ANI 1953
 
0.3%
(Missing) 71877
 
11.3%

Length

2025-05-29T02:03:27.128595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:27.218769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
igac 482167
29.3%
contrato 482167
29.3%
2932/2008 434289
26.3%
ani 59339
 
3.6%
wfs 57386
 
3.5%
2130/2014 47878
 
2.9%
google 29370
 
1.8%
maps 29370
 
1.8%
dc 5528
 
0.3%
criterio 5528
 
0.3%
Other values (6) 15293
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 1398623
 
10.8%
1085841
 
8.4%
o 1039989
 
8.0%
C 975390
 
7.5%
t 971815
 
7.5%
0 964334
 
7.4%
A 543459
 
4.2%
I 541506
 
4.2%
a 515443
 
4.0%
G 511537
 
3.9%
Other values (34) 4450093
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12998030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1398623
 
10.8%
1085841
 
8.4%
o 1039989
 
8.0%
C 975390
 
7.5%
t 971815
 
7.5%
0 964334
 
7.4%
A 543459
 
4.2%
I 541506
 
4.2%
a 515443
 
4.0%
G 511537
 
3.9%
Other values (34) 4450093
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12998030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1398623
 
10.8%
1085841
 
8.4%
o 1039989
 
8.0%
C 975390
 
7.5%
t 971815
 
7.5%
0 964334
 
7.4%
A 543459
 
4.2%
I 541506
 
4.2%
a 515443
 
4.0%
G 511537
 
3.9%
Other values (34) 4450093
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12998030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1398623
 
10.8%
1085841
 
8.4%
o 1039989
 
8.0%
C 975390
 
7.5%
t 971815
 
7.5%
0 964334
 
7.4%
A 543459
 
4.2%
I 541506
 
4.2%
a 515443
 
4.0%
G 511537
 
3.9%
Other values (34) 4450093
34.2%

globalid
Text

Missing 

Distinct204
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
2025-05-29T02:03:27.494971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters20249064
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row39e4fdfc-e270-4c5b-831f-66a95421c314
2nd row39e4fdfc-e270-4c5b-831f-66a95421c314
3rd row39e4fdfc-e270-4c5b-831f-66a95421c314
4th row39e4fdfc-e270-4c5b-831f-66a95421c314
5th row39e4fdfc-e270-4c5b-831f-66a95421c314
ValueCountFrequency (%)
82f3fc98-ce8b-46f8-b7a6-3b5cf1963682 8118
 
1.4%
f476c2a1-cda0-4ea4-9488-c987c8b0d072 7519
 
1.3%
e4c5e5c8-c417-4f9c-9c61-11ec5dbbf14f 7519
 
1.3%
7b572e28-5971-46f0-9149-db0a1da2347a 7519
 
1.3%
1c3d52fe-9046-4f66-888f-ecff9a25e02f 7519
 
1.3%
4d1909a3-470e-4ec5-9e25-53b9435c1119 7519
 
1.3%
5fe45486-39cd-4f49-8d1f-c66d7478a412 7519
 
1.3%
c1e0651f-bfcb-4d13-bb23-b355f5a0829b 7519
 
1.3%
57c4993a-e211-4e47-90af-4d1ec15a4a9f 7519
 
1.3%
b5acb2d9-e193-400e-8ab2-2aaf8070bc94 7519
 
1.3%
Other values (194) 486685
86.5%
2025-05-29T02:03:27.871774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2249896
 
11.1%
4 1727987
 
8.5%
9 1251012
 
6.2%
5 1161461
 
5.7%
b 1143300
 
5.6%
0 1133383
 
5.6%
1 1120579
 
5.5%
a 1110718
 
5.5%
c 1101282
 
5.4%
8 1095007
 
5.4%
Other values (7) 7154439
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20249064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2249896
 
11.1%
4 1727987
 
8.5%
9 1251012
 
6.2%
5 1161461
 
5.7%
b 1143300
 
5.6%
0 1133383
 
5.6%
1 1120579
 
5.5%
a 1110718
 
5.5%
c 1101282
 
5.4%
8 1095007
 
5.4%
Other values (7) 7154439
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20249064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2249896
 
11.1%
4 1727987
 
8.5%
9 1251012
 
6.2%
5 1161461
 
5.7%
b 1143300
 
5.6%
0 1133383
 
5.6%
1 1120579
 
5.5%
a 1110718
 
5.5%
c 1101282
 
5.4%
8 1095007
 
5.4%
Other values (7) 7154439
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20249064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2249896
 
11.1%
4 1727987
 
8.5%
9 1251012
 
6.2%
5 1161461
 
5.7%
b 1143300
 
5.6%
0 1133383
 
5.6%
1 1120579
 
5.5%
a 1110718
 
5.5%
c 1101282
 
5.4%
8 1095007
 
5.4%
Other values (7) 7154439
35.3%

nombretramo
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
Por Defnir
562474 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5624740
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPor Defnir
2nd rowPor Defnir
3rd rowPor Defnir
4th rowPor Defnir
5th rowPor Defnir

Common Values

ValueCountFrequency (%)
Por Defnir 562474
88.7%
(Missing) 71877
 
11.3%

Length

2025-05-29T02:03:27.994605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:28.056705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
por 562474
50.0%
defnir 562474
50.0%

Most occurring characters

ValueCountFrequency (%)
r 1124948
20.0%
P 562474
10.0%
o 562474
10.0%
562474
10.0%
D 562474
10.0%
e 562474
10.0%
f 562474
10.0%
n 562474
10.0%
i 562474
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1124948
20.0%
P 562474
10.0%
o 562474
10.0%
562474
10.0%
D 562474
10.0%
e 562474
10.0%
f 562474
10.0%
n 562474
10.0%
i 562474
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1124948
20.0%
P 562474
10.0%
o 562474
10.0%
562474
10.0%
D 562474
10.0%
e 562474
10.0%
f 562474
10.0%
n 562474
10.0%
i 562474
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1124948
20.0%
P 562474
10.0%
o 562474
10.0%
562474
10.0%
D 562474
10.0%
e 562474
10.0%
f 562474
10.0%
n 562474
10.0%
i 562474
10.0%

created_user
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
SIG_INVIAS
562474 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5624740
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSIG_INVIAS
2nd rowSIG_INVIAS
3rd rowSIG_INVIAS
4th rowSIG_INVIAS
5th rowSIG_INVIAS

Common Values

ValueCountFrequency (%)
SIG_INVIAS 562474
88.7%
(Missing) 71877
 
11.3%

Length

2025-05-29T02:03:28.126483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:28.183841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sig_invias 562474
100.0%

Most occurring characters

ValueCountFrequency (%)
I 1687422
30.0%
S 1124948
20.0%
G 562474
 
10.0%
_ 562474
 
10.0%
N 562474
 
10.0%
V 562474
 
10.0%
A 562474
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1687422
30.0%
S 1124948
20.0%
G 562474
 
10.0%
_ 562474
 
10.0%
N 562474
 
10.0%
V 562474
 
10.0%
A 562474
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1687422
30.0%
S 1124948
20.0%
G 562474
 
10.0%
_ 562474
 
10.0%
N 562474
 
10.0%
V 562474
 
10.0%
A 562474
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1687422
30.0%
S 1124948
20.0%
G 562474
 
10.0%
_ 562474
 
10.0%
N 562474
 
10.0%
V 562474
 
10.0%
A 562474
 
10.0%

created_date
Date

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
Minimum2025-05-02 05:01:01
Maximum2025-05-02 05:01:01
Invalid dates0
Invalid dates (%)0.0%
2025-05-29T02:03:28.233669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:03:28.305243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

last_edited_user
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
SIG_INVIAS
562474 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5624740
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSIG_INVIAS
2nd rowSIG_INVIAS
3rd rowSIG_INVIAS
4th rowSIG_INVIAS
5th rowSIG_INVIAS

Common Values

ValueCountFrequency (%)
SIG_INVIAS 562474
88.7%
(Missing) 71877
 
11.3%

Length

2025-05-29T02:03:28.395596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T02:03:28.453704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sig_invias 562474
100.0%

Most occurring characters

ValueCountFrequency (%)
I 1687422
30.0%
S 1124948
20.0%
G 562474
 
10.0%
_ 562474
 
10.0%
N 562474
 
10.0%
V 562474
 
10.0%
A 562474
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1687422
30.0%
S 1124948
20.0%
G 562474
 
10.0%
_ 562474
 
10.0%
N 562474
 
10.0%
V 562474
 
10.0%
A 562474
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1687422
30.0%
S 1124948
20.0%
G 562474
 
10.0%
_ 562474
 
10.0%
N 562474
 
10.0%
V 562474
 
10.0%
A 562474
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5624740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1687422
30.0%
S 1124948
20.0%
G 562474
 
10.0%
_ 562474
 
10.0%
N 562474
 
10.0%
V 562474
 
10.0%
A 562474
 
10.0%

last_edited_date
Date

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Memory size4.8 MiB
Minimum2025-05-02 05:01:01
Maximum2025-05-02 05:01:01
Invalid dates0
Invalid dates (%)0.0%
2025-05-29T02:03:28.502025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:03:28.583248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

territorial
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct24
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean15.215948
Minimum0
Maximum28
Zeros29885
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:28.670572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median14
Q325
95-th percentile28
Maximum28
Range28
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.7212671
Coefficient of variation (CV)0.63888673
Kurtosis-1.3210166
Mean15.215948
Median Absolute Deviation (MAD)10
Skewness-0.11730072
Sum8558575
Variance94.503034
MonotonicityNot monotonic
2025-05-29T02:03:29.104348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
28 108408
17.1%
12 63356
10.0%
1 53614
8.5%
11 45862
 
7.2%
25 38406
 
6.1%
16 30359
 
4.8%
0 29885
 
4.7%
14 23727
 
3.7%
24 21396
 
3.4%
23 18397
 
2.9%
Other values (14) 129064
20.3%
(Missing) 71877
11.3%
ValueCountFrequency (%)
0 29885
4.7%
1 53614
8.5%
2 11107
 
1.8%
3 16729
 
2.6%
4 10824
 
1.7%
5 17051
 
2.7%
7 2019
 
0.3%
9 8511
 
1.3%
11 45862
7.2%
12 63356
10.0%
ValueCountFrequency (%)
28 108408
17.1%
26 3066
 
0.5%
25 38406
 
6.1%
24 21396
 
3.4%
23 18397
 
2.9%
22 12836
 
2.0%
21 14301
 
2.3%
20 5653
 
0.9%
18 9190
 
1.4%
17 3176
 
0.5%

shape__length
Real number (ℝ)

Missing 

Distinct204
Distinct (%)< 0.1%
Missing71877
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean35859.262
Minimum57.251086
Maximum187543.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-05-29T02:03:29.842135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum57.251086
5-th percentile185.7228
Q12787.6523
median20288.041
Q359955.463
95-th percentile111391.58
Maximum187543.56
Range187486.31
Interquartile range (IQR)57167.811

Descriptive statistics

Standard deviation39621.887
Coefficient of variation (CV)1.1049276
Kurtosis1.5950185
Mean35859.262
Median Absolute Deviation (MAD)18999.524
Skewness1.3444294
Sum2.0169903 × 1010
Variance1.5698939 × 109
MonotonicityNot monotonic
2025-05-29T02:03:30.001681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93718.4475 8118
 
1.3%
38368.02634 7519
 
1.2%
4350.726746 7519
 
1.2%
2442.963321 7519
 
1.2%
17688.58664 7519
 
1.2%
28213.22049 7519
 
1.2%
57.25108628 7519
 
1.2%
2675.503671 7519
 
1.2%
66485.17894 7519
 
1.2%
67125.05935 7519
 
1.2%
Other values (194) 486685
76.7%
(Missing) 71877
 
11.3%
ValueCountFrequency (%)
57.25108628 7519
1.2%
60.11756091 1474
 
0.2%
77.79665568 1474
 
0.2%
78.24250373 2327
 
0.4%
79.58696033 1520
 
0.2%
89.32030806 1474
 
0.2%
94.09577605 1474
 
0.2%
119.955042 2215
 
0.3%
141.257527 1474
 
0.2%
147.5303928 666
 
0.1%
ValueCountFrequency (%)
187543.5632 826
 
0.1%
182615.2639 2000
 
0.3%
171893.496 5899
0.9%
167668.3091 3192
0.5%
145732.2463 896
 
0.1%
138865.755 2016
 
0.3%
132217.2231 1306
 
0.2%
127407.0039 1428
 
0.2%
117609.5109 2461
0.4%
111760.7257 4201
0.7%

mes
Date

Distinct125
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Minimum2014-01-01 00:00:00
Maximum2024-05-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-29T02:03:30.145185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:03:30.282161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-05-29T02:02:53.682643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:21.645632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:24.850008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:27.698675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:31.409962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:33.285715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:37.650406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:39.729923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:42.502565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:45.249619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:49.100802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:51.914155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:53.855367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:22.248738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:24.999834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:27.845089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:31.568335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:34.131781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:38.036082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:40.810532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:42.654250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:45.409639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:49.256367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:52.065025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:54.012036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:23.065470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:25.165642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:28.002336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:31.726283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:34.797013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:38.187861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:40.984884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:42.811281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:45.571758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:49.414578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:52.220890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:54.163527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:23.420088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:25.646243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:28.153222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:31.870901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:35.083792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:38.324475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:41.131631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:42.960379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:45.727589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:49.556280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:52.358978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:54.319636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:23.619844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:26.455625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:28.314015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:32.034327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:35.284487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:38.476030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:41.282706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:43.228456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:45.893771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:49.716371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:52.515939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:54.725850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:23.766553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:26.608570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:28.792482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:32.190938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:35.486670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:38.623292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:41.440670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:44.202357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:46.443455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:49.889124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:52.657646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:55.580660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:23.924213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:26.758314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:30.499799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:32.340349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:35.701102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:38.773918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:41.592673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:44.353054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:46.967477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:50.041671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:52.799496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:55.736161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:24.081226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:26.908217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:30.651494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:32.500886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:35.919435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:38.940245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:41.737071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:44.500167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:47.720449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:50.199079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:52.938263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:55.892323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:24.236822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:27.063166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:30.804794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:32.658862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:36.126297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:39.104571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:41.882013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:44.640900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:48.327321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:50.361495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:53.080151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:56.053063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:24.388371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:27.217247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:30.958570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:32.818280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:36.277349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:39.259626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:42.043264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:44.792356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:48.597984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:50.585324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:53.231110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:56.215592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:24.535121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:27.387224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:31.115546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:32.969585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:36.735959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:39.412010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:42.193266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:44.935652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:48.778768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:51.608992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:53.374994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:56.359086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:24.691992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:27.537412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:31.255366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:33.119517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:37.499380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:39.561424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:42.334820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:45.094425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:48.931401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:51.754836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T02:02:53.532100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-29T02:03:30.415916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
administradorcalzadacantidadevasorescantidadexentos787cantidadtraficocategoriacategoriatarifadistancia_finaldistancia_inicialfuentegrupo_administrador_vialidpeajenombre_rutaobjectidposte_de_referencia_finalposte_de_referencia_inicialrutashape__lengthsuperficieterritorialvalortarifa
administrador1.0000.1550.0000.0000.0300.1210.0680.2350.1470.1780.8240.2530.3360.0920.2470.3150.3350.3060.1370.3440.047
calzada0.1551.0000.0000.0020.0780.6250.1260.2460.1660.4670.2270.3190.4790.5840.4080.4360.4840.2360.1880.4320.053
cantidadevasores0.0000.0001.0000.1480.1460.0000.005-0.027-0.0450.0050.0000.0410.016-0.019-0.025-0.0260.0160.0430.0000.030-0.070
cantidadexentos7870.0000.0020.1481.0000.7300.0000.0060.0210.0230.0060.000-0.0210.013-0.075-0.025-0.0240.0160.0070.0000.038-0.090
cantidadtrafico0.0300.0780.1460.7301.0000.0510.183-0.0030.0060.0780.056-0.0380.0780.0070.002-0.0060.0750.0250.0370.0450.174
categoria0.1210.6250.0000.0000.0511.0000.0960.1730.1600.4140.1460.3130.4410.4560.3300.3230.4000.3560.0970.3340.044
categoriatarifa0.0680.1260.0050.0060.1830.0961.0000.0750.0640.0930.1240.1390.1320.0980.0910.0800.1220.0950.0930.1070.348
distancia_final0.2350.246-0.0270.021-0.0030.1730.0751.0000.1690.2110.2390.0570.373-0.125-0.0230.0770.388-0.1110.119-0.1390.029
distancia_inicial0.1470.166-0.0450.0230.0060.1600.0640.1691.0000.1480.284-0.1210.3160.0020.1150.4390.333-0.2090.126-0.0870.016
fuente0.1780.4670.0050.0060.0780.4140.0930.2110.1481.0000.3490.2840.4020.3190.3230.2920.4240.2650.3380.2450.074
grupo_administrador_vial0.8240.2270.0000.0000.0560.1460.1240.2390.2840.3491.0000.3390.4430.1150.4910.3990.5730.3920.4500.6070.099
idpeaje0.2530.3190.041-0.021-0.0380.3130.1390.057-0.1210.2840.3391.0000.509-0.086-0.041-0.1080.5190.2010.2720.1600.014
nombre_ruta0.3360.4790.0160.0130.0780.4410.1320.3730.3160.4020.4430.5091.0000.3070.4030.3610.8700.3690.5170.4940.106
objectid0.0920.584-0.019-0.0750.0070.4560.098-0.1250.0020.3190.115-0.0860.3071.0000.1330.2080.312-0.2230.0830.166-0.088
poste_de_referencia_final0.2470.408-0.025-0.0250.0020.3300.091-0.0230.1150.3230.491-0.0410.4030.1331.0000.6450.3790.2950.308-0.107-0.055
poste_de_referencia_inicial0.3150.436-0.026-0.024-0.0060.3230.0800.0770.4390.2920.399-0.1080.3610.2080.6451.0000.355-0.3170.329-0.144-0.050
ruta0.3350.4840.0160.0160.0750.4000.1220.3880.3330.4240.5730.5190.8700.3120.3790.3551.0000.3530.8310.5010.100
shape__length0.3060.2360.0430.0070.0250.3560.095-0.111-0.2090.2650.3920.2010.369-0.2230.295-0.3170.3531.0000.1840.0120.006
superficie0.1370.1880.0000.0000.0370.0970.0930.1190.1260.3380.4500.2720.5170.0830.3080.3290.8310.1841.0000.2890.063
territorial0.3440.4320.0300.0380.0450.3340.107-0.139-0.0870.2450.6070.1600.4940.166-0.107-0.1440.5010.0120.2891.0000.063
valortarifa0.0470.053-0.070-0.0900.1740.0440.3480.0290.0160.0740.0990.0140.106-0.088-0.055-0.0500.1000.0060.0630.0631.000

Missing values

2025-05-29T02:02:57.158151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-29T02:03:00.412871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-29T02:03:07.171184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idpeajepeajecategoriatarifadesdehastavalortarifacantidadtraficocantidadevasorescantidadexentos787nombre_peajecodigo_tramoubicacionobjectidcategoriaposte_de_referencia_inicialdistancia_inicialposte_de_referencia_finaldistancia_finalnombre_rutasectoradministradorgrupo_administrador_vialsuperficiecalzadarutafuenteglobalidnombretramocreated_usercreated_datelast_edited_userlast_edited_dateterritorialshape__lengthmes
01ALVARADOI2015-08-212015-08-317000277300.0660.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-08
11ALVARADOII2015-08-212015-08-31760099300.070.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-08
21ALVARADOIII2015-08-212015-08-311610012770.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-08
31ALVARADOIV2015-08-212015-08-312040020740.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-08
41ALVARADOV2015-08-212015-08-312290041670.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-08
51ALVARADOEA2015-08-212015-08-31720070.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-08
61ALVARADOEG2015-08-212015-08-31530080.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-08
71ALVARADOER2015-08-212015-08-316900450.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-08
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91ALVARADOI2015-09-012015-09-307000847311.01959.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-09
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idpeajepeajecategoriatarifadesdehastavalortarifacantidadtraficocantidadevasorescantidadexentos787nombre_peajecodigo_tramoubicacionobjectidcategoriaposte_de_referencia_inicialdistancia_inicialposte_de_referencia_finaldistancia_finalnombre_rutasectoradministradorgrupo_administrador_vialsuperficiecalzadarutafuenteglobalidnombretramocreated_usercreated_datelast_edited_userlast_edited_dateterritorialshape__lengthmes# duplicates
01ALVARADOEA2015-08-212015-08-31720070.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-082
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21ALVARADOEA2015-10-012015-10-317200310.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882015-102
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81ALVARADOEA2016-03-012016-03-317700140.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882016-032
91ALVARADOEA2016-04-012016-04-177700160.00.0alvarado4305Vía Andalucía – Y de Cerritos PR2506 Km 86475.010.00.0105.0193.0Alternas a la Troncal del MagdalenaIbagué - Mariquita2.0NaN32.043IGAC (Contrato 2130/2014)39e4fdfc-e270-4c5b-831f-66a95421c314Por DefnirSIG_INVIAS5/2/2025 5:01:01 AMSIG_INVIAS5/2/2025 5:01:01 AM24.0105584.3948882016-042